ee11be2582
1. Test JARs are built & published 1. log4j.resources is explicitly excluded. Without this, downstream test run logging depends on the order the JARs are listed/loaded 1. sql/hive pulls in spark-sql &...spark-catalyst for its test runs 1. The copied in test classes were rm'd, and a test edited to remove its now duplicate assert method 1. Spark streaming is now build with the same plugin/phase as the rest, but its shade plugin declaration is kept in (so different from the rest of the test plugins). Due to (#2), this means the test JAR no longer includes its log4j file. Outstanding issues: * should the JARs be shaded? `spark-streaming-test.jar` does, but given these are test jars for developers only, especially in the same spark source tree, it's hard to justify. * `maven-jar-plugin` v 2.6 was explicitly selected; without this the apache-1.4 parent template JAR version (2.4) chosen. * Are there any other resources to exclude? Author: Steve Loughran <stevel@hortonworks.com> Closes #5119 from steveloughran/stevel/patches/SPARK-6433-test-jars and squashes the following commits: 81ceb01 [Steve Loughran] SPARK-6433 add a clearer comment explaining what the plugin is doing & why a6dca33 [Steve Loughran] SPARK-6433 : pull configuration section form archive plugin c2b5f89 [Steve Loughran] SPARK-6433 omit "jar" goal from jar plugin fdac51b [Steve Loughran] SPARK-6433 -002; indentation & delegate plugin version to parent 650f442 [Steve Loughran] SPARK-6433 patch 001: test JARs are built; sql/hive pulls in spark-sql & spark-catalyst for its test runs |
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| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| README.md | ||
Spark SQL
This module provides support for executing relational queries expressed in either SQL or a LINQ-like Scala DSL.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst’s logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
Other dependencies for developers
In order to create new hive test cases , you will need to set several environmental variables.
export HIVE_HOME="<path to>/hive/build/dist"
export HIVE_DEV_HOME="<path to>/hive/"
export HADOOP_HOME="<path to>/hadoop-1.0.4"
Using the console
An interactive scala console can be invoked by running build/sbt hive/console.
From here you can execute queries with HiveQl and manipulate DataFrame by using DSL.
catalyst$ build/sbt hive/console
[info] Starting scala interpreter...
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.dsl._
import org.apache.spark.sql.catalyst.errors._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules._
import org.apache.spark.sql.catalyst.util._
import org.apache.spark.sql.execution
import org.apache.spark.sql.functions._
import org.apache.spark.sql.hive._
import org.apache.spark.sql.hive.test.TestHive._
import org.apache.spark.sql.types._
Type in expressions to have them evaluated.
Type :help for more information.
scala> val query = sql("SELECT * FROM (SELECT * FROM src) a")
query: org.apache.spark.sql.DataFrame = org.apache.spark.sql.DataFrame@74448eed
Query results are DataFrames and can be operated as such.
scala> query.collect()
res2: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,val_311], [27,val_27]...
You can also build further queries on top of these DataFrames using the query DSL.
scala> query.where('key > 30).select(avg('key)).collect()
res3: Array[org.apache.spark.sql.Row] = Array([274.79025423728814])